Deep-learning classifier understands free-text radiology reports

Free-text radiology reports can be automatically classified by convolutional neural networks (CNNs) powered by deep-learning algorithms with accuracy that's equal to or better than that achieved by traditional--and decidedly labor-intensive--natural language processing (NLP) methods. That's the conclusion of researchers led by Matthew Lungren, MD, MPH, of Stanford University. The team tested a CNN model they developed for mining pulmonary-embolism findings from thoracic CT reports generated at two institutions. Radiology published their study, lead-authored by Matthew Chen, MS, also of Stanford, online Nov. 13. The researchers analyzed annotations made by two radiologists for the presence, chronicity and location of pulmonary embolisms, then compared their CNN's performance with that of an NLP model considered quite proficient in this task, called PeFinder. They note that PeFinder and similar existing NLP techniques demand a "relatively high burden of development, including domain-specific feature engineering, complex annotations and laborious coding for specific tasks."